Characterising within-hospital SARS-CoV-2 transmission events using epidemiological and viral genomic data across two pandemic waves

Hospital outbreaks of COVID19 result in considerable mortality and disruption to healthcare services and yet little is known about transmission within this setting. We characterise within hospital transmission by combining viral genomic and epidemiological data using Bayesian modelling amongst 2181 patients and healthcare workers from a large UK NHS Trust. Transmission events were compared between Wave 1 (1st March to 25th July 2020) and Wave 2 (30th November 2020 to 24th January 2021). We show that staff-to-staff transmissions reduced from 31.6% to 12.9% of all infections. Patient-to-patient transmissions increased from 27.1% to 52.1%. 40%-50% of hospital-onset patient cases resulted in onward transmission compared to 4% of community-acquired cases. Control measures introduced during the pandemic likely reduced transmissions between healthcare workers but were insufficient to prevent increasing numbers of patient-to-patient transmissions. As hospital-acquired cases drive most onward transmission, earlier identification of nosocomial cases will be required to break hospital transmission chains.

Editorial Note: This manuscript has been previously reviewed at another journal that is not operating a transparent peer review scheme. This document only contains reviewer comments and rebuttal letters for versions considered at Nature Communications.

REVIEWERS' COMMENTS
Reviewer #1 (Remarks to the Author): For reference, my prior comments are listed on the response memo under reviewer 1. The authors have fully addressed my concerns and have appropriately framed the limitations I previously highlighted.
To briefly summarize, then, this is an interesting and well-conducted analysis that has been significantly strengthened by these revisions. It has important implications for understanding inhospital transmission dynamics, and uses a relatively novel approach for the hospital setting that combines epidemiologic and genomic data. The importance of staff-to-staff transmission, nosocomial > community acquired cases driving secondary cases, and preponderance of transmission within certain wards with high numbers of beds within bays are critical insights that can guide improvements in infection control practices, particularly in the setting of the moretransmissible Delta variant.
One point of interest since my last review, in a similar analysis of the outbreak in Provincetown, Massachusetts (https://doi.org/10.1101/2021.10.20.21265137), those investigators also used outbreaker2 and were able to incorporate use of iSNVs to infer directionality of transmission. While I don't think this necessarily needs to be included in this paper, it may be interesting to look at in subsequent analyses to better understand transmission between staff and patients, for example. I congratulate the authors on this work and recommend accepting this manuscript for publication.

Aaron Richterman
Reviewer #3 (Remarks to the Author): Many thanks for the opportunity to review this revised manuscript, and many thanks to the other journal and Nature for allowing the transfer of my previous comments. A sensible approach that saves time. All my comments on a previous version of this manuscript (reviewed for the version submitted to another journal) have now been appropriately addressed. The only remaining changes that I think are needed are to address a few typos and minor notational issues in the SI: p2 typo: "and and infecteé " p4 "for example as given by proximity *or* the number of staff shared between them" p5 I am happy with the the definition of X(sigma) at the top of p5 in the sense that I can see what all the elements are and they make sense, but the notation for defining the matrix (with no separators between matrix elements or lines) is new to me and seems unnecessarily confusing (and the new text in fact makes the first line of page 5 redundant). Of course, this could also just be a Word formatting issue! p6 " as these infection events are not linked *to* either of the observed cases" p9 The equation at top of this page seems to have a couple of typos -please check (could also be a Word formatting issues again). p13 typo: "We first calculated, for each patient, i, the probability that they acquired we observed a delay between …"

REVIEWERS' COMMENTS
Reviewer #1 (Remarks to the Author): For reference, my prior comments are listed on the response memo under reviewer 1. The authors have fully addressed my concerns and have appropriately framed the limitations I previously highlighted.
To briefly summarize, then, this is an interesting and well-conducted analysis that has been significantly strengthened by these revisions. It has important implications for understanding inhospital transmission dynamics, and uses a relatively novel approach for the hospital setting that combines epidemiologic and genomic data. The importance of staff-to-staff transmission, nosocomial > community acquired cases driving secondary cases, and preponderance of transmission within certain wards with high numbers of beds within bays are critical insights that can guide improvements in infection control practices, particularly in the setting of the moretransmissible Delta variant.
One point of interest since my last review, in a similar analysis of the outbreak in Provincetown, Massachusetts (https://doi.org/10.1101/2021.10.20.21265137), those investigators also used outbreaker2 and were able to incorporate use of iSNVs to infer directionality of transmission. While I don't think this necessarily needs to be included in this paper, it may be interesting to look at in subsequent analyses to better understand transmission between staff and patients, for example. I congratulate the authors on this work and recommend accepting this manuscript for publication.

Aaron Richterman
Response: Thank you for your kind comments and for taking the time to review our paper. We will review the recommended paper and consider incorporating it into our methods in the future.
Reviewer #3 (Remarks to the Author): Many thanks for the opportunity to review this revised manuscript, and many thanks to the other journal and Nature for allowing the transfer of my previous comments. A sensible approach that saves time. Response: Thank you for the kind comments. We have fixed the referencing error highlighted.
Reviewer #4 (Remarks to the Author): All my comments on a previous version of this manuscript (reviewed for the version submitted to another journal) have now been appropriately addressed. The only remaining changes that I think are needed are to address a few typos and minor notational issues in the SI: p2 typo: "and and infecteé " p4 "for example as given by proximity *or* the number of staff shared between them" p5 I am happy with the the definition of X(sigma) at the top of p5 in the sense that I can see what all the elements are and they make sense, but the notation for defining the matrix (with no separators between matrix elements or lines) is new to me and seems unnecessarily confusing (and the new text in fact makes the first line of page 5 redundant). Of course, this could also just be a Word formatting issue! p6 " as these infection events are not linked *to* either of the observed cases" p9 The equation at top of this page seems to have a couple of typos -please check (could also be a Word formatting issues again). p13 typo: "We first calculated, for each patient, i, the probability that they acquired we observed a delay between …" Response: Thank you for the thorough review of our paper and spotting these typos/formatting errors. We've now corrected all of these.